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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sofka, M. Jingdan Zhang Zhou, S.K. Comaniciu, D. |
| Copyright Year | 2010 |
| Description | Author affiliation: Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540, USA (Sofka, M.; Jingdan Zhang; Zhou, S.K.; Comaniciu, D.) |
| Abstract | In this paper, we propose a novel framework for detecting multiple objects in 2D and 3D images. Since a joint multi-object model is difficult to obtain in most practical situations, we focus here on detecting the objects sequentially, one-by-one. The interdependence of object poses and strong prior information embedded in our domain of medical images results in better performance than detecting the objects individually. Our approach is based on Sequential Estimation techniques, frequently applied to visual tracking. Unlike in tracking, where the sequential order is naturally determined by the time sequence, the order of detection of multiple objects must be selected, leading to a Hierarchical Detection Network (HDN). We present an algorithm that optimally selects the order based on probability of states (object poses) within the ground truth region. The posterior distribution of the object pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple objects and hierarchical levels. We show on 2D ultrasound images of left atrium, that the automatically selected sequential order yields low mean detection error. We also quantitatively evaluate the hierarchical detection of fetal faces and three fetal brain structures in 3D ultrasound images. |
| Starting Page | 1735 |
| Ending Page | 1742 |
| File Size | 2372852 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Object detection Monte Carlo methods Performance evaluation History Educational institutions Machine vision Radio frequency System testing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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